winequality_ML_model_as_pycaret.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/bugeki/portfolio_projects_of_data_analysis/blob/main/winequality_ML_model_as_pycaret.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "719695b5",
      "metadata": {
        "id": "719695b5"
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "15b12844",
      "metadata": {
        "id": "15b12844"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv(\"winequality-red.csv\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bbe993ec",
      "metadata": {
        "id": "bbe993ec",
        "outputId": "179c04c8-d2be-41e9-9950-0fcc25109618"
      },
      "outputs": [
        {
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
              "      <th>residual sugar</th>\n",
              "      <th>chlorides</th>\n",
              "      <th>free sulfur dioxide</th>\n",
              "      <th>total sulfur dioxide</th>\n",
              "      <th>density</th>\n",
              "      <th>pH</th>\n",
              "      <th>sulphates</th>\n",
              "      <th>alcohol</th>\n",
              "      <th>quality</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.880</td>\n",
              "      <td>0.00</td>\n",
              "      <td>2.6</td>\n",
              "      <td>0.098</td>\n",
              "      <td>25.0</td>\n",
              "      <td>67.0</td>\n",
              "      <td>0.99680</td>\n",
              "      <td>3.20</td>\n",
              "      <td>0.68</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>7.8</td>\n",
              "      <td>0.760</td>\n",
              "      <td>0.04</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.092</td>\n",
              "      <td>15.0</td>\n",
              "      <td>54.0</td>\n",
              "      <td>0.99700</td>\n",
              "      <td>3.26</td>\n",
              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.99800</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1599 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0               7.4             0.700         0.00             1.9      0.076   \n",
              "1               7.8             0.880         0.00             2.6      0.098   \n",
              "2               7.8             0.760         0.04             2.3      0.092   \n",
              "3              11.2             0.280         0.56             1.9      0.075   \n",
              "4               7.4             0.700         0.00             1.9      0.076   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1596            6.3             0.510         0.13             2.3      0.076   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
              "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
              "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
              "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol  quality  \n",
              "0         9.4        5  \n",
              "1         9.8        5  \n",
              "2         9.8        5  \n",
              "3         9.8        6  \n",
              "4         9.4        5  \n",
              "...       ...      ...  \n",
              "1594     10.5        5  \n",
              "1595     11.2        6  \n",
              "1596     11.0        6  \n",
              "1597     10.2        5  \n",
              "1598     11.0        6  \n",
              "\n",
              "[1599 rows x 12 columns]"
            ]
          },
          "execution_count": 15,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "926ee37f",
      "metadata": {
        "id": "926ee37f",
        "outputId": "4a587367-2081-405e-cee3-ce1927dc195d"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "fixed acidity           0\n",
              "volatile acidity        0\n",
              "citric acid             0\n",
              "residual sugar          0\n",
              "chlorides               0\n",
              "free sulfur dioxide     0\n",
              "total sulfur dioxide    0\n",
              "density                 0\n",
              "pH                      0\n",
              "sulphates               0\n",
              "alcohol                 0\n",
              "quality                 0\n",
              "dtype: int64"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.isnull().sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "151e6acd",
      "metadata": {
        "id": "151e6acd",
        "outputId": "233f95b7-00fb-441e-86b1-a634f58cf685"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "5    681\n",
              "6    638\n",
              "7    199\n",
              "4     53\n",
              "8     18\n",
              "3     10\n",
              "Name: quality, dtype: int64"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"quality\"].value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7f50595e",
      "metadata": {
        "id": "7f50595e"
      },
      "outputs": [],
      "source": [
        "df.loc[df[\"quality\"]>=6, \"Target\"] = \"High Quality\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "981de97f",
      "metadata": {
        "id": "981de97f",
        "outputId": "b468ab47-3019-41c9-837e-6b0e0b2c737b"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "High Quality    855\n",
              "NaN             744\n",
              "Name: Target, dtype: int64"
            ]
          },
          "execution_count": 19,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df[\"Target\"].value_counts(dropna=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "caa2b981",
      "metadata": {
        "id": "caa2b981"
      },
      "outputs": [],
      "source": [
        "df[\"Target\"] = df[\"Target\"].fillna(\"Low Quality\")"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "7b099b7d",
      "metadata": {
        "id": "7b099b7d",
        "outputId": "99f1c564-aba3-4f6f-9557-d0cb98797b94"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "High Quality    53.470919\n",
              "Low Quality     46.529081\n",
              "Name: Target, dtype: float64"
            ]
          },
          "execution_count": 26,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.Target.value_counts(normalize=True) * 100"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "1602b1d3",
      "metadata": {
        "id": "1602b1d3",
        "outputId": "30f8070c-42e5-4976-d6cd-58d537edb512"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "<AxesSubplot:>"
            ]
          },
          "execution_count": 24,
          "metadata": {},
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          },
          "output_type": "display_data"
        }
      ],
      "source": [
        "df.Target.value_counts().plot(kind=\"bar\")"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "f934352d",
      "metadata": {
        "id": "f934352d"
      },
      "source": [
        "# Metrik Seçimi"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "8cb10712",
      "metadata": {
        "id": "8cb10712"
      },
      "source": [
        "# accuracy as metric  "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "524f8218",
      "metadata": {
        "id": "524f8218",
        "outputId": "24d2874d-e030-4fcb-f1de-57db7bbcbd6a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(1599, 13)"
            ]
          },
          "execution_count": 30,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "ff6b229e",
      "metadata": {
        "id": "ff6b229e"
      },
      "outputs": [],
      "source": [
        "df = df.drop(\"quality\", axis=1)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "5f7fadf0",
      "metadata": {
        "id": "5f7fadf0"
      },
      "outputs": [],
      "source": [
        "X = df.drop(\"Target\", axis=1)\n",
        "y = df.Target"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "13a2d7b2",
      "metadata": {
        "id": "13a2d7b2"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "93fc101c",
      "metadata": {
        "id": "93fc101c"
      },
      "outputs": [],
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42, stratify=y)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "b68433af",
      "metadata": {
        "scrolled": true,
        "id": "b68433af",
        "outputId": "3e171b76-df6c-466f-a927-b76742245af4"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "High Quality    0.534793\n",
              "Low Quality     0.465207\n",
              "Name: Target, dtype: float64"
            ]
          },
          "execution_count": 62,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_train.value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f352d55f",
      "metadata": {
        "id": "f352d55f",
        "outputId": "c5ee0985-08a8-44d4-d548-bf72f15d36c9"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "High Quality    0.534375\n",
              "Low Quality     0.465625\n",
              "Name: Target, dtype: float64"
            ]
          },
          "execution_count": 63,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "y_test.value_counts(normalize=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "12d6c2e6",
      "metadata": {
        "id": "12d6c2e6"
      },
      "outputs": [],
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "from sklearn.svm import SVC\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.linear_model import LogisticRegression\n",
        "from sklearn.ensemble import GradientBoostingClassifier, RandomForestClassifier\n",
        "from xgboost import XGBClassifier"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "35549f18",
      "metadata": {
        "id": "35549f18"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import cross_val_score, StratifiedKFold"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "543ed5b3",
      "metadata": {
        "id": "543ed5b3"
      },
      "outputs": [],
      "source": [
        "skf = StratifiedKFold(n_splits=2)\n",
        "results = cross_val_score(lasso, X, y, cv=skf)\n",
        "cross_val_score()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "4410afea",
      "metadata": {
        "id": "4410afea"
      },
      "outputs": [],
      "source": [
        "cross_val_score()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "0296432b",
      "metadata": {
        "id": "0296432b"
      },
      "outputs": [],
      "source": [
        "models = []\n",
        "models.append((KNeighborsClassifier(), \"KNN\"))\n",
        "models.append((SVC(), \"SVC\"))\n",
        "models.append((DecisionTreeClassifier(), \"DT\"))\n",
        "models.append((LogisticRegression(), \"LR\"))\n",
        "models.append((GradientBoostingClassifier(), \"GB\"))\n",
        "models.append((RandomForestClassifier(), \"RF\"))\n",
        "models.append((XGBClassifier(verbosity = 0), \"XGB\"))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "130d4ecd",
      "metadata": {
        "id": "130d4ecd",
        "outputId": "e34b6f78-c029-400b-c4a5-afdeef9de786"
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-2.8.3.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "import cufflinks as cf\n",
        "import plotly.offline\n",
        "\n",
        "cf.go_offline()\n",
        "cf.set_config_file(offline=False, world_readable=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "97163ec3",
      "metadata": {
        "id": "97163ec3",
        "outputId": "cd32570a-1a96-4c1f-b85b-707e20d8fd36"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "KNN: 0.632\n",
            "SVC: 0.618\n",
            "DT: 0.725\n",
            "LR: 0.744\n",
            "GB: 0.773\n",
            "RF: 0.804\n",
            "XGB: 0.783\n"
          ]
        },
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KNN</th>\n",
              "      <td>0.662763</td>\n",
              "      <td>0.610329</td>\n",
              "      <td>0.622066</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.592506</td>\n",
              "      <td>0.629108</td>\n",
              "      <td>0.633803</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DT</th>\n",
              "      <td>0.709602</td>\n",
              "      <td>0.748826</td>\n",
              "      <td>0.715962</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR</th>\n",
              "      <td>0.744731</td>\n",
              "      <td>0.758216</td>\n",
              "      <td>0.730047</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GB</th>\n",
              "      <td>0.744731</td>\n",
              "      <td>0.802817</td>\n",
              "      <td>0.772300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RF</th>\n",
              "      <td>0.765808</td>\n",
              "      <td>0.840376</td>\n",
              "      <td>0.805164</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGB</th>\n",
              "      <td>0.775176</td>\n",
              "      <td>0.793427</td>\n",
              "      <td>0.781690</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            1         2         3\n",
              "KNN  0.662763  0.610329  0.622066\n",
              "SVC  0.592506  0.629108  0.633803\n",
              "DT   0.709602  0.748826  0.715962\n",
              "LR   0.744731  0.758216  0.730047\n",
              "GB   0.744731  0.802817  0.772300\n",
              "RF   0.765808  0.840376  0.805164\n",
              "XGB  0.775176  0.793427  0.781690"
            ]
          },
          "execution_count": 112,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results = []\n",
        "names = []\n",
        "\n",
        "\n",
        "for model, name in models:\n",
        "    estimator = model\n",
        "    skf = StratifiedKFold(n_splits=3)\n",
        "    result = cross_val_score(estimator, X_train, y_train, cv=skf, scoring=\"accuracy\")\n",
        "    print(f\"{name}: {result.mean().round(3)}\")\n",
        "    results.append(result)\n",
        "    names.append(name)\n",
        "    \n",
        "results_df = pd.DataFrame(results, index=names, columns=[i for i in range(1, len(result)+1)])\n",
        "results_df"
      ]
    },
    {
      "cell_type": "code",
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      "metadata": {
        "id": "a9919216",
        "outputId": "7990c2cb-9566-4353-ed7f-136ea06e843b"
      },
      "outputs": [
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      "source": [
        "results_df.T.iplot(kind=\"box\", boxpoints=\"all\")"
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    },
    {
      "cell_type": "markdown",
      "id": "2719353c",
      "metadata": {
        "id": "2719353c"
      },
      "source": [
        "# Pipeline"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d19f0b6e",
      "metadata": {
        "id": "d19f0b6e"
      },
      "outputs": [],
      "source": [
        "from sklearn.compose import ColumnTransformer\n",
        "from sklearn.pipeline import Pipeline\n",
        "from sklearn.preprocessing import StandardScaler, OneHotEncoder, PowerTransformer, MinMaxScaler, RobustScaler"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eee1b0f4",
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        "id": "eee1b0f4",
        "outputId": "4123b556-2d42-425a-9196-d244effa018c"
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              "      <th>fixed acidity</th>\n",
              "      <th>volatile acidity</th>\n",
              "      <th>citric acid</th>\n",
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              "      <th>total sulfur dioxide</th>\n",
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              "      <th>Target</th>\n",
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              "    <tr>\n",
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              "      <td>54.0</td>\n",
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              "      <td>0.65</td>\n",
              "      <td>9.8</td>\n",
              "      <td>Low Quality</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>11.2</td>\n",
              "      <td>0.280</td>\n",
              "      <td>0.56</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.075</td>\n",
              "      <td>17.0</td>\n",
              "      <td>60.0</td>\n",
              "      <td>0.99800</td>\n",
              "      <td>3.16</td>\n",
              "      <td>0.58</td>\n",
              "      <td>9.8</td>\n",
              "      <td>High Quality</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>7.4</td>\n",
              "      <td>0.700</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.9</td>\n",
              "      <td>0.076</td>\n",
              "      <td>11.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>0.99780</td>\n",
              "      <td>3.51</td>\n",
              "      <td>0.56</td>\n",
              "      <td>9.4</td>\n",
              "      <td>Low Quality</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>6.2</td>\n",
              "      <td>0.600</td>\n",
              "      <td>0.08</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.090</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99490</td>\n",
              "      <td>3.45</td>\n",
              "      <td>0.58</td>\n",
              "      <td>10.5</td>\n",
              "      <td>Low Quality</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1595</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.550</td>\n",
              "      <td>0.10</td>\n",
              "      <td>2.2</td>\n",
              "      <td>0.062</td>\n",
              "      <td>39.0</td>\n",
              "      <td>51.0</td>\n",
              "      <td>0.99512</td>\n",
              "      <td>3.52</td>\n",
              "      <td>0.76</td>\n",
              "      <td>11.2</td>\n",
              "      <td>High Quality</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1596</th>\n",
              "      <td>6.3</td>\n",
              "      <td>0.510</td>\n",
              "      <td>0.13</td>\n",
              "      <td>2.3</td>\n",
              "      <td>0.076</td>\n",
              "      <td>29.0</td>\n",
              "      <td>40.0</td>\n",
              "      <td>0.99574</td>\n",
              "      <td>3.42</td>\n",
              "      <td>0.75</td>\n",
              "      <td>11.0</td>\n",
              "      <td>High Quality</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1597</th>\n",
              "      <td>5.9</td>\n",
              "      <td>0.645</td>\n",
              "      <td>0.12</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.075</td>\n",
              "      <td>32.0</td>\n",
              "      <td>44.0</td>\n",
              "      <td>0.99547</td>\n",
              "      <td>3.57</td>\n",
              "      <td>0.71</td>\n",
              "      <td>10.2</td>\n",
              "      <td>Low Quality</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1598</th>\n",
              "      <td>6.0</td>\n",
              "      <td>0.310</td>\n",
              "      <td>0.47</td>\n",
              "      <td>3.6</td>\n",
              "      <td>0.067</td>\n",
              "      <td>18.0</td>\n",
              "      <td>42.0</td>\n",
              "      <td>0.99549</td>\n",
              "      <td>3.39</td>\n",
              "      <td>0.66</td>\n",
              "      <td>11.0</td>\n",
              "      <td>High Quality</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1599 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
              "0               7.4             0.700         0.00             1.9      0.076   \n",
              "1               7.8             0.880         0.00             2.6      0.098   \n",
              "2               7.8             0.760         0.04             2.3      0.092   \n",
              "3              11.2             0.280         0.56             1.9      0.075   \n",
              "4               7.4             0.700         0.00             1.9      0.076   \n",
              "...             ...               ...          ...             ...        ...   \n",
              "1594            6.2             0.600         0.08             2.0      0.090   \n",
              "1595            5.9             0.550         0.10             2.2      0.062   \n",
              "1596            6.3             0.510         0.13             2.3      0.076   \n",
              "1597            5.9             0.645         0.12             2.0      0.075   \n",
              "1598            6.0             0.310         0.47             3.6      0.067   \n",
              "\n",
              "      free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
              "0                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "1                    25.0                  67.0  0.99680  3.20       0.68   \n",
              "2                    15.0                  54.0  0.99700  3.26       0.65   \n",
              "3                    17.0                  60.0  0.99800  3.16       0.58   \n",
              "4                    11.0                  34.0  0.99780  3.51       0.56   \n",
              "...                   ...                   ...      ...   ...        ...   \n",
              "1594                 32.0                  44.0  0.99490  3.45       0.58   \n",
              "1595                 39.0                  51.0  0.99512  3.52       0.76   \n",
              "1596                 29.0                  40.0  0.99574  3.42       0.75   \n",
              "1597                 32.0                  44.0  0.99547  3.57       0.71   \n",
              "1598                 18.0                  42.0  0.99549  3.39       0.66   \n",
              "\n",
              "      alcohol        Target  \n",
              "0         9.4   Low Quality  \n",
              "1         9.8   Low Quality  \n",
              "2         9.8   Low Quality  \n",
              "3         9.8  High Quality  \n",
              "4         9.4   Low Quality  \n",
              "...       ...           ...  \n",
              "1594     10.5   Low Quality  \n",
              "1595     11.2  High Quality  \n",
              "1596     11.0  High Quality  \n",
              "1597     10.2   Low Quality  \n",
              "1598     11.0  High Quality  \n",
              "\n",
              "[1599 rows x 12 columns]"
            ]
          },
          "execution_count": 146,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "bad5a264",
      "metadata": {
        "id": "bad5a264"
      },
      "outputs": [],
      "source": [
        "preprocessor = ColumnTransformer(transformers=[('boxcox',\n",
        "                                 Pipeline(steps=[('scaler', RobustScaler())]),\n",
        "                                 \n",
        "                                ['fixed acidity', 'volatile acidity',\n",
        "                                  'citric acid']),\n",
        "                                               \n",
        "                                            ('standard',\n",
        "                                             Pipeline(steps=[('scaler', MinMaxScaler())]),\n",
        "                                             ['residual sugar', 'chlorides',\n",
        "                                              'free sulfur dioxide', 'total sulfur dioxide',\n",
        "                                              'density', 'pH', 'sulphates', 'alcohol'])\n",
        "                                              ])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "eb435d79",
      "metadata": {
        "id": "eb435d79",
        "outputId": "b68cf8bd-0b7e-47a8-e48b-d5f6cc0d1f94"
      },
      "outputs": [
        {
          "ename": "NameError",
          "evalue": "name 'bazılarına' is not defined",
          "output_type": "error",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m/var/folders/5j/p27n3znx1gd4vp6l3wtnk03c0000gn/T/ipykernel_2056/2817151677.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m col = ColumnTransformer(transformers=[\n\u001b[0;32m----> 2\u001b[0;31m     \u001b[0;34m(\u001b[0m\u001b[0;34m\"scaler\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStandardScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbazılarına\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m\"minmax\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mMinMaxScaler\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mbazılarına\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;34m(\u001b[0m\u001b[0;34m\"ohe\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mOneHotEncoder\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mcategorical\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mNameError\u001b[0m: name 'bazılarına' is not defined"
          ]
        }
      ],
      "source": [
        "col = ColumnTransformer(transformers=[\n",
        "    (\"scaler\", StandardScaler(), [bazılarına]),\n",
        "    (\"minmax\", MinMaxScaler(), [bazılarına]),\n",
        "    (\"ohe\", OneHotEncoder(), [categorical])\n",
        "    \n",
        "])\n",
        "\n",
        "\n",
        "estimator = Pipeline(\n",
        "    \n",
        "    steps=[\n",
        "        (\"a\", col),\n",
        "        (\"model\", model())\n",
        "    ]\n",
        "\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "58c09b0a",
      "metadata": {
        "id": "58c09b0a",
        "outputId": "60ed9a70-4f5e-4fe2-c920-d01e1da11c4c"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "KNN: 0.678\n",
            "SVC: 0.739\n",
            "DT: 0.698\n",
            "LR: 0.745\n",
            "GB: 0.77\n",
            "RF: 0.771\n",
            "XGB: 0.774\n"
          ]
        },
        {
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              "<div>\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>1</th>\n",
              "      <th>2</th>\n",
              "      <th>3</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>KNN</th>\n",
              "      <td>0.658080</td>\n",
              "      <td>0.683099</td>\n",
              "      <td>0.692488</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVC</th>\n",
              "      <td>0.723653</td>\n",
              "      <td>0.753521</td>\n",
              "      <td>0.739437</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DT</th>\n",
              "      <td>0.658080</td>\n",
              "      <td>0.699531</td>\n",
              "      <td>0.737089</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LR</th>\n",
              "      <td>0.740047</td>\n",
              "      <td>0.741784</td>\n",
              "      <td>0.753521</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GB</th>\n",
              "      <td>0.747073</td>\n",
              "      <td>0.772300</td>\n",
              "      <td>0.791080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RF</th>\n",
              "      <td>0.754098</td>\n",
              "      <td>0.788732</td>\n",
              "      <td>0.769953</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGB</th>\n",
              "      <td>0.763466</td>\n",
              "      <td>0.779343</td>\n",
              "      <td>0.779343</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "            1         2         3\n",
              "KNN  0.658080  0.683099  0.692488\n",
              "SVC  0.723653  0.753521  0.739437\n",
              "DT   0.658080  0.699531  0.737089\n",
              "LR   0.740047  0.741784  0.753521\n",
              "GB   0.747073  0.772300  0.791080\n",
              "RF   0.754098  0.788732  0.769953\n",
              "XGB  0.763466  0.779343  0.779343"
            ]
          },
          "execution_count": 144,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "results = []\n",
        "names = []\n",
        "\n",
        "\n",
        "for model, name in models:\n",
        "    estimator = Pipeline(\n",
        "        steps=[(\"preprocessor\", preprocessor), (\"classifier\", model)]\n",
        "    )\n",
        "    skf = StratifiedKFold(n_splits=3)\n",
        "    result = cross_val_score(estimator, X_train, y_train, cv=skf, scoring=\"accuracy\")\n",
        "    print(f\"{name}: {result.mean().round(3)}\")\n",
        "    results.append(result)\n",
        "    names.append(name)\n",
        "    \n",
        "results_df = pd.DataFrame(results, index=names, columns=[i for i in range(1, len(result)+1)])\n",
        "results_df"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f77619dd",
      "metadata": {
        "id": "f77619dd",
        "outputId": "118f446b-47ba-4d3d-d9a6-fa9510f27075"
      },
      "outputs": [
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